AI Request for Quote (RFQ) and Proposal Generation Workflow

This workflow automates the costly bottleneck of manually responding to complex Requests for Quote (RFQs). By deploying specialized AI agents, it extracts requirements from unstructured documents, retrieves relevant specifications from a Product Information Management (PIM) or ERP system like SAP, and assembles compliant proposals using approved templates. The operational upside comes from compressing a multi-day, multi-person process into a consistent, auditable workflow, enabling sales teams to respond to more opportunities with higher quality and faster turnaround, directly improving win rates and resource leverage.

Implementation requires orchestrating document AI (e.g., Azure Form Recognizer), retrieval-augmented generation (RAG) against technical databases, and integration with CPQ and CRM platforms like Salesforce. Critical controls include mandatory human review gates for high-value bids, exception routing for ambiguous requirements, and a full audit trail of all AI-generated content and decisions. Rollout is sequenced by RFQ complexity, starting with standardized product categories before expanding to custom engineering bids, ensuring governance and accuracy are preserved.

The operational bottleneck is the manual, high-latency process of extracting requirements from lengthy RFQ documents and assembling compliant proposals from fragmented knowledge bases. A custom multi-agent workflow automates this by deploying specialized agents for document AI, knowledge retrieval, and template assembly, orchestrated by a central controller. This reduces response time from days to hours and ensures consistency, directly improving bid throughput and competitive positioning in high-volume procurement cycles.

Implementation requires integrating the orchestrator with enterprise systems like SAP or Salesforce for live product data and historical pricing. The Document Parsing Agent uses LLMs with retrieval-augmented generation (RAG) against internal wikis and past bids. Critical controls include human review gates for high-value proposals, confidence scoring on extracted terms, and an immutable audit log for compliance. Deployment is typically containerized, using frameworks like LangGraph for agent state management, ensuring the system scales across thousands of RFQs while preserving governance.

Phase 1 establishes the core document AI and data extraction pipeline. Incoming RFQs from email, portals, or procurement systems are ingested. A specialized agent uses vision models and LLMs to parse complex PDFs, extracting key requirements, technical specifications, and commercial terms. This data is structured and validated against a product knowledge graph and historical bid database, immediately reducing manual data entry by 60-80% and standardizing requirement capture. The output is a clean, structured data package ready for proposal assembly.

Phase 2 automates template assembly and initial draft generation. Orchestrators like LangGraph route the structured data to a proposal agent, which retrieves approved boilerplate, past successful proposals, and compliant technical language from a vector database. A first draft is generated, incorporating company branding and mandatory clauses. Phase 3 integrates with CPQ and ERP systems (e.g., Salesforce, SAP) to inject dynamic pricing, terms, and timelines. The final workflow includes mandatory human review gates for legal and commercial approval before automated delivery back through the buyer's channel, ensuring governance and accuracy while slashing response time from days to hours.

A production-grade AI RFQ workflow automates the extraction of complex requirements from bid documents and the assembly of compliant, tailored proposals. The operational upside comes from compressing response cycles from weeks to hours, improving bid consistency, and freeing sales engineers for high-value negotiation. However, uncontrolled automation risks mispricing, non-compliant terms, and brand misrepresentation. The solution architecture must embed human-in-the-loop controls at critical decision gates, such as final pricing approval and legal term validation, before any external submission.

Implementation follows a phased rollout, starting with a single product line or region to validate extraction accuracy and pricing logic. Integration with Salesforce CPQ, SAP, or Oracle ERP is critical for accessing approved price lists and customer history. Observability is built through detailed audit logs of every AI decision and human override, ensuring full traceability for compliance. This staged approach de-risks the deployment, allowing the workflow to scale across the organization while maintaining financial and legal governance.